The current explosion in sensor data has brought a tipping point in information, surveillance, and reconnaissance technologies. This problem can be addressed through the insertion of novel artificial intelligence-based methodologies. The nature of the problem is that heterogeneous data (i.e., structured and unstructured) must be reduced in size by an order of magnitude prior to dissemination.
The scope of the problem is to define a novel computational intelligence methodology, which can learn to map distributed heterogeneous data to actionable meaning for dissemination. This methodology must be practical and successfully scale for development. A need exists for a system and corresponding methodology that provides for autonomous evolution of improved features.